CN115618540A - Wind generating set optimal layout method based on three-level dynamic variation rate - Google Patents

Wind generating set optimal layout method based on three-level dynamic variation rate Download PDF

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CN115618540A
CN115618540A CN202211383446.8A CN202211383446A CN115618540A CN 115618540 A CN115618540 A CN 115618540A CN 202211383446 A CN202211383446 A CN 202211383446A CN 115618540 A CN115618540 A CN 115618540A
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optimization
fan
grid
variation rate
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沈宏涛
杨富程
韩二红
胡伟成
李航
王彬滨
刘海坤
黄博文
李天�
袁紫婷
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Sichuan Electric Power Design and Consulting Co Ltd
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    • G06F30/10—Geometric CAD
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Abstract

The invention discloses a wind generating set optimal layout method based on three-level dynamic variation rate, which comprises the following steps: the method comprises the following steps: acquiring target wind power plant information, wherein the target wind power plant information comprises (1) annual average wind resource information of a target wind power plant; (2) unit specification parameters; (3) wind farm planning zone boundaries; step two: calculating the annual total average power generation and evaluating the full life cycle power cost by combining the fan wake flow speed and the turbulence model; step three: gridding a planning region of a wind power plant, taking the leveled energy cost as an optimization target, performing unit grid position preliminary optimization by adopting an improved genetic optimization algorithm based on three-level dynamic variation rate, and obtaining a plurality of grid optimization schemes through an integration theory; step four: and constructing the grid boundary of each candidate unit according to the preliminary optimization result of the grid position, and performing fine coordinate position optimization in the corresponding grid by adopting a self-adaptive weight particle swarm optimization algorithm to obtain a final unit layout scheme.

Description

Wind generating set optimal layout method based on three-level dynamic variation rate
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a wind generating set optimal layout method based on three-level dynamic variation rate.
Background
With the exhaustion of traditional fossil energy, the demand for wind energy development is increasing day by day. In order to furthest improve the power generation benefit of the wind power plant, one of the core problems is to reasonably optimize the layout scheme of the units before the construction of the wind power plant so as to reduce the power consumption cost of the wind power plant in the whole life cycle. However, due to wake flow influence among the units, the fan layout scheme optimizing process is very complex, and it is difficult to obtain a global optimal layout scheme. Genetic and particle swarm optimization are the most common combined optimization algorithms, and are widely used for optimization solution of fan layout schemes. The existing single genetic and particle swarm algorithm calculates all individuals of a population, the consumed time is very long, the variation rate is fixed, the algorithm is easy to fall into local optimization, the obtained fan layout scheme is not optimal, and a very large promotion space is still reserved in the optimization of the layout scheme. Therefore, how to simultaneously improve the efficiency of the optimization algorithm and obtain the global optimal solution as much as possible is very critical to improving the effect of the fan layout scheme.
Disclosure of Invention
In view of this, the invention aims to provide a wind generating set optimal layout method based on three-level dynamic variation rate, which can reasonably optimize a set layout scheme so as to reduce the power consumption cost of a wind power plant in the whole life cycle.
In order to achieve the purpose, the invention provides the following technical scheme:
a wind generating set optimal layout method based on three-level dynamic variation rate comprises the following steps:
the method comprises the following steps: obtaining target wind farm information, comprising:
(1) The annual average wind resource information of the target wind power plant is drawn according to annual statistical wind speed and direction information in the target wind electric field area;
(2) The unit specification parameters comprise the height of a hub, the diameter of an impeller and a power thrust curve;
(3) The method comprises the following steps of (1) defining a wind power plant planning region boundary comprising a regular boundary or an irregular boundary;
step two: calculating the annual total average power generation and evaluating the full life cycle power cost by combining the fan wake flow speed and the turbulence model;
step three: gridding a planning region of a wind power plant, taking the leveled energy cost as an optimization target, performing unit grid position preliminary optimization by adopting an improved genetic optimization algorithm based on three-level dynamic variation rate, and obtaining a plurality of grid optimization schemes through an integration theory;
step four: and constructing a grid boundary of each candidate unit according to the preliminary optimization result of the grid position, and carrying out fine coordinate position optimization in a corresponding grid by adopting a self-adaptive weight particle swarm optimization algorithm to obtain a final unit layout scheme.
Further, in the second step, the method for calculating the annual total average power generation amount and evaluating the power cost of the whole life cycle comprises the following steps:
21 Based on the Gaussian fan wake flow model and the turbulence model, calculating the wind speed loss and turbulence enhancement of each wind driven generator after being influenced by the upstream fan wake flow;
22 Estimating the inflow wind speed at the hub height of each downstream wind driven generator by adopting a classic wake flow superposition principle, calculating the annual energy production of each wind driven generator, and summing the annual energy production of each wind driven generator to obtain the annual total energy production of the wind power plant;
23 The annual average distribution of the wind speed in the full wind direction and the total generating capacity in the full life cycle of the wind power plant are calculated, and a standardized energy cost objective function is constructed by combining economic benefit indexes comprising unit component manufacturing cost, wind power plant operation and maintenance cost and fund recovery coefficient.
Further, in the step 21), the gaussian fan wake model is:
Figure BDA0003929554610000021
Figure BDA0003929554610000022
Figure BDA0003929554610000023
Figure BDA0003929554610000024
wherein δ is wake wind deficit; delta hub Is a wheel hubLoss of wake wind speed at altitude; σ is the standard deviation of the wake wind speed profile; d is the fan impeller diameter; y and z are horizontal and vertical coordinates, respectively; z is a radical of hub Is the fan hub height; c T Is the thrust coefficient; k is a radical of * Is the wake attenuation coefficient; x represents the horizontal distance of the downstream fan from the upstream fan; epsilon represents a parameter related to the thrust coefficient;
the calculation method of the wake turbulence of the fan comprises the following steps:
Figure BDA0003929554610000025
wherein, I + Is additional wake turbulence; k n Is a model constant;
the fan wake flow is superposed as follows:
Figure BDA0003929554610000026
wherein v is i And v j Is the inflow wind speed at the upstream and downstream fans; v. of 0 The incoming flow wind speed at infinity; v. of ij The inflow wind speed caused by the upstream fan at the downstream fan.
Further, in step 22), for the inflow conditions of a given speed v and direction θ, the total power generation power of the wind farm is calculated according to the power curve of the unit, and in combination with the probability density function of the statistical average wind condition, the annual average power generation power is predicted as:
Figure BDA0003929554610000031
wherein N is t Is the number of fans; n is a radical of θ Is the wind direction interval division number; n is a radical of v Is the wind speed interval division number; f. of j Is the wind direction interval probability; p i At the wind speed v k Generating power by the unit; (X, Y) is unit coordinate information; p is a radical of formula j Wind speed and direction joint distribution probability, v k Is a wind speed interval division point; theta j Indicating the wind direction angle represented by the jth wind speed.
Further, in the step 23), the flattening energy cost objective function is:
Figure BDA0003929554610000032
wherein, C total The cost of the unit is reduced; c f Is the capital recovery factor; c O&M The operation and maintenance cost of the wind power plant; AEP is the annual average total power generation of the wind farm.
Further, the third step includes the following steps:
31 Dividing a planning area of the wind power plant into grid distribution maps with different sizes, and defaulting the coordinate position of a candidate unit to be a grid center;
32 An improved genetic optimization algorithm of three-level dynamic variation rate is adopted, the improved variation rate of the current fan installation number, the population individual repetition rate and the current iteration step is considered at the same time, so that the global optimal search capability is improved, the local optimal search is avoided, the gridded layout scheme is optimized preliminarily, and a plurality of grid optimization schemes are obtained through an integration theory.
Further, in the step 32), the method for performing preliminary optimization of the unit grid position by using the improved genetic optimization algorithm based on the three-level dynamic variation rate includes:
321 Given that there are two possibilities for each grid center, namely, installation of a fan and no installation of a fan, the corresponding "genes" are respectively coded as "1" and "0", and each individual corresponds to a fan arrangement scheme consisting of a string of "genes";
322 Random initialization population): for each "gene", the random amplitude is "1" or "0", and the number of iterations i step ＝0；
323 Computing individual fitness based on unique processing to obtain completely nonrepeating population individuals;
judging whether the individual fitness is smaller than a set threshold value: if so, obtaining a fan layout grid optimization scheme; if not, then i step ＝i step +1, go to step 324);
324 Random individual selection using wheel selection;
325 Random crossing is carried out on every two individuals, namely, the genes at the corresponding positions of the two individuals are interchanged;
326 Random variation for each individual "gene";
327 ) judge i step Whether or not it is equal to the set value N s If yes, obtaining a fan layout grid optimization scheme; if not, step 323) is executed in a loop.
Further, in the step 326), the "gene" of each individual is randomly mutated with an improved mutation rate, wherein the improved mutation rate is:
Figure BDA0003929554610000041
Figure BDA0003929554610000042
wherein p is 0 A mutation rate of 0 coding positions; p is a radical of formula 1 1 is the rate of variation of the coding position; n is the number of grids;
Figure BDA0003929554610000043
is the average population number; 1 and 2 respectively representing the initial variation rate and the final variation rate; representing the repetition rate of individuals in the population; representing the number of steps of the current iteration; the total number of iteration steps is indicated.
Further, the fourth step includes the following steps:
41 Calculating the central coordinates and boundary conditions of the grids where the candidate set is located in all the grid optimization schemes;
42 Taking the set center coordinates of the grid optimization scheme as the initial population of the particles;
43 Setting boundary conditions as constraint conditions, and calculating a particle fitness function by taking the normalized energy cost as an optimization target;
judging whether the particle fitness function is smaller than a set threshold value: if so, obtaining a layout scheme of the wind generating set; if not, adding 1 to the iteration number, and executing the step 44);
44 Judging whether the iteration number reaches the set maximum iteration number: if yes, obtaining a layout scheme of the wind generating set; if not, updating the particle population by adopting a self-adaptive weight particle swarm optimization algorithm, and executing the step 43).
Further, in the step 44), the adaptive weight particle swarm optimization algorithm is:
Figure BDA0003929554610000044
Figure BDA0003929554610000045
wherein the content of the first and second substances,
Figure BDA0003929554610000046
is the position of the ith particle at the time of the t-th iteration;
Figure BDA0003929554610000047
is the velocity vector of the ith particle at the t-th iteration; r is 1 And r 2 Is a random number between 0 and 1; p is a radical of i And p g The best candidate solutions found for the ith particle and the entire population, respectively; beta is a i And beta g The parameters are self-defined parameters and respectively control the development capability and the exploration capability of particle motion; omega a Is the nonlinear dynamic inertial weight adjusted according to the distance to the global optimal solution during the optimization process.
The invention has the beneficial effects that:
the invention relates to a wind generating set optimal layout method based on three-level dynamic variation rate, which provides a two-stage optimization thought combining grid and coordinate optimization aiming at the problems that the fan layout optimization is low in calculation efficiency and easy to fall into local optimization, improves the calculation efficiency under the condition of ensuring high precision, and simultaneously provides the three-level dynamic variation rate to avoid the optimization process from falling into the local optimization as much as possible, the provided high-efficiency optimization algorithm can obtain a fan optimization scheme better than the traditional single algorithm to a great extent, the method greatly improves the calculation efficiency of optimization solution, and simultaneously obtains the overall optimal fan layout result as much as possible; the wind generating set optimal layout method based on the three-level dynamic variation rate can reasonably optimize the set layout scheme so as to reduce the power consumption cost of the wind power plant in the whole life cycle.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a flow chart of a method for optimizing the layout of a wind turbine generator system according to the present disclosure;
FIG. 2 is a flow chart of a genetic particle swarm fusion optimization algorithm;
FIG. 3 is a target wind farm planning area map;
FIG. 4 is a comparison of fan layout optimization schemes; (a) a fan map obtained by a genetic algorithm; (b) obtaining a fan layout by a particle swarm algorithm; (c) a fan layout obtained by the fusion algorithm of the invention;
fig. 5 is a box plot drawn by statistical computational analysis of 10 sub-optimization processes and results.
The present embodiment
The present invention is further described with reference to the following drawings and specific examples so that those skilled in the art can better understand the present invention and can practice the present invention, but the examples are not intended to limit the present invention.
As shown in fig. 1, the method for optimizing layout of a wind turbine generator system based on three-level dynamic variation rate in this embodiment includes the following steps:
the method comprises the following steps: acquiring target wind power plant information, comprising:
(1) Drawing a rose diagram according to annual statistical wind speed and direction information in a target wind electric field region according to annual average wind resource information of the target wind power plant;
(2) The unit specification parameters comprise the height of a hub, the diameter of an impeller and a power thrust curve;
(3) And a wind power plant planning area boundary which is used for dividing a wind power plant planning area boundary comprising a regular boundary or an irregular boundary.
Step two: and calculating the annual total average power generation and evaluating the full life cycle power cost by combining the fan wake flow speed and the turbulence model. Specifically, the method for calculating the annual total average power generation and evaluating the full life cycle power cost comprises the following steps:
21 Based on the gaussian fan wake model and the turbulence model, the wind speed loss and turbulence enhancement of each wind turbine generator after being influenced by the upstream fan wake are calculated.
Specifically, the gaussian fan wake model is:
Figure BDA0003929554610000051
Figure BDA0003929554610000061
Figure BDA0003929554610000062
Figure BDA0003929554610000063
wherein δ is wake wind deficit; delta hub The wake flow wind speed loss at the hub height is obtained; σ is the standard deviation of the wake wind speed profile; d is the fan impeller diameter; y and z are horizontal and vertical coordinates, respectively; z is a radical of hub Is the fan hub height; c T Is the thrust coefficient; k is a radical of * Is the wake attenuation coefficient; x represents the horizontal distance of the downstream fan from the upstream fan; epsilon represents a parameter related to the thrust coefficient;
the calculation method of the wake turbulence of the fan comprises the following steps:
Figure BDA0003929554610000064
wherein, I + Is additional wake turbulence; k n Is a model constant;
the fan wake flow is superposed as follows:
Figure BDA0003929554610000065
wherein v is i And v j Is the inflow wind speed at the upstream and downstream fans; v. of 0 The incoming flow wind speed at infinity; v. of ij The inflow wind speed caused by the upstream fan at the downstream fan.
22 The annual energy generation amount of each wind driven generator is summed to obtain the annual total energy generation amount of the wind power plant.
Specifically, for the inflow conditions of a given speed v and a given direction theta, the total power generation power of the wind power plant is calculated according to a unit power curve, and the annual average power generation power is predicted by combining a probability density function of a statistical average wind condition as follows:
Figure BDA0003929554610000066
wherein N is t Is the number of fans; n is a radical of θ Is the wind direction interval division number; n is a radical of v Is the wind speed interval division number; f. of j Is the wind direction interval probability; p i Is at the wind speed v k The single unit is set to generate power; (X, Y) is unit coordinate information; p is a radical of j Wind speed and direction joint distribution probability, v k Is a wind speed interval division point; theta j Indicating the wind direction angle represented by the jth wind speed.
23 The annual average distribution of the wind speed in the full wind direction and the total generating capacity in the full life cycle of the wind power plant are calculated, and a standardized energy cost objective function is constructed by combining economic benefit indexes comprising unit component manufacturing cost, wind power plant operation and maintenance cost and fund recovery coefficient.
Specifically, the flattening energy cost objective function is:
Figure BDA0003929554610000071
wherein, C total The unit cost; c f Is the capital recovery factor; c O&M The operation and maintenance cost of the wind power plant; AEP is the annual average total power generation of the wind farm.
Step three: the method comprises the steps of meshing a planning region of the wind power plant, using the leveled energy cost as an optimization target, adopting an improved genetic optimization algorithm based on three-level dynamic variation rate to carry out unit grid position preliminary optimization, and obtaining a plurality of grid optimization schemes through an integration theory. The method comprises the following steps:
31 Divide the wind farm planning area into grid distribution maps of different sizes, and the coordinate position of the candidate set is defaulted as the grid center.
32 The improved genetic optimization algorithm of the three-level dynamic variation rate is adopted, the improved variation rate of the current fan installation number, the population individual repetition rate and the current iteration step are considered at the same time, so that the global optimal search capability is improved, the local optimal situation is avoided, the gridded layout scheme is preliminarily optimized, and a plurality of grid optimization schemes are obtained through an integration theory.
Specifically, as shown in fig. 2, the method for performing preliminary optimization of the grid position of the unit by using the improved genetic optimization algorithm based on the three-level dynamic variation rate includes:
321 Given that there are two possibilities for each grid center, namely, installation of a fan and no installation of a fan, the corresponding "genes" are respectively coded as "1" and "0", and each individual corresponds to a fan arrangement scheme consisting of a string of "genes";
322 Random initialization population): for each "gene", the random amplitude is "1" or "0", and the number of iterations i step ＝0；
323 Computing individual fitness based on unique processing to obtain completely nonrepeating population individuals;
judging whether the individual fitness is smaller than a set threshold value: if so, obtaining a fan layout grid optimization scheme; if not, then i step ＝i step +1, go to step 324);
324 Random individual selection using wheel selection;
325 Random crossing is carried out on every two individuals, namely, the genes at the corresponding positions of the two individuals are interchanged;
326 Random variation of "genes" for each individual;
326 ) judge i step Whether or not it is equal to the set value N s If so, obtaining a fan layout grid optimization scheme; if not, step 323) is executed in a loop.
In this example, the "gene" of each individual was randomly mutated using an improved mutation rate, which was:
Figure BDA0003929554610000072
Figure BDA0003929554610000081
wherein p is 0 A variance ratio of 0 coding positions; p is a radical of 1 1 is the rate of variation of the coding position; n is the number of grids;
Figure BDA0003929554610000082
is the average population number; 1 and 2 respectively representing the initial variation rate and the final variation rate; representing the repetition rate of individuals in the population, and dividing the unique number of individuals by the total number of the population to obtain the repetition rate; representing the number of steps of the current iteration; the total number of iteration steps is indicated.
Step four: and constructing the grid boundary of each candidate unit according to the preliminary optimization result of the grid position, and performing fine coordinate position optimization in the corresponding grid by adopting a self-adaptive weight particle swarm optimization algorithm to obtain a final unit layout scheme. As shown in fig. 2, the method comprises the following steps:
41 Calculating the central coordinates and boundary conditions of the grids where the candidate set is located in all the grid optimization schemes;
42 Taking the set center coordinates of the grid optimization scheme as the initial population of the particles;
43 Setting boundary conditions as constraint conditions, and calculating a particle fitness function by taking the normalized energy cost as an optimization target;
judging whether the particle fitness function is smaller than a set threshold value: if so, obtaining a layout scheme of the wind generating set; if not, adding 1 to the number of iterations, and executing step 44);
44 Judging whether the iteration number reaches the set maximum iteration number: if so, obtaining a layout scheme of the wind generating set; if not, updating the particle population by adopting a self-adaptive weight particle swarm optimization algorithm, and executing the step 43).
The self-adaptive weight particle swarm optimization algorithm comprises the following steps:
Figure BDA0003929554610000083
Figure BDA0003929554610000084
wherein the content of the first and second substances,
Figure BDA0003929554610000085
is the position of the ith particle at the time of the t-th iteration;
Figure BDA0003929554610000086
is the velocity vector of the ith particle at the t-th iteration; r is 1 And r 2 Is a random number between 0 and 1; p is a radical of i And p g The best candidate solutions found for the ith particle and the entire population, respectively; beta is a i And beta g Is a self-defined parameter, and respectively controls the development capability and the exploration capability of particle motion；ω a Is the nonlinear dynamic inertial weight adjusted according to the distance to the global optimal solution during the optimization process.
Case description
The wind power plant area with the regularization boundary is taken as a research object, the wind generating set optimization layout method provided by the embodiment is adopted to carry out set arrangement, and the planning area and the reference machine position of the wind generating set are shown in fig. 3.
And acquiring annual average wind speed and direction distribution information by combining long-term observation wind speed data of the target wind power plant. Then, a fan layout optimization scheme is performed by respectively adopting a single optimization algorithm and a genetic particle swarm fusion algorithm, the arrangement schemes obtained by the two algorithms are shown in fig. 4, wherein fig. 4 (a) and 4 (b) are fan layout diagrams of the genetic algorithm and the particle swarm algorithm respectively, and fig. 4 (c) is a fan layout diagram of the genetic particle swarm fusion algorithm.
Various parameters of the three fan arrangement schemes are compared, as shown in table 1. As can be seen from Table 1, compared with a single optimization algorithm, the provided fusion optimization algorithm can effectively reduce the iteration times and ensure the precision of the optimization result. In general, the efficient algorithm of the fan layout scheme can reduce the time consumption of calculation and can better improve the fan layout optimization effect.
TABLE 1 Fan layout plan parameter comparison
Figure BDA0003929554610000091
As can be seen from table 1, the fusion optimization algorithm provided in this embodiment can not only improve 6% to 10% of total generated power while ensuring the optimization effect of the electricity consumption cost, but also reduce 25% to 40% of iterative computation time, in the case of considering both the generated energy of the wind farm and the electricity consumption cost. In particular, the present example takes a 10-degree optimal process and result statistical analysis method, and plots a box plot as shown in FIG. 5, with the abscissa being the method and the ordinate being the cost of electricity. As can be seen from fig. 5, compared with the other two methods, the solution of the fusion optimization algorithm is more stable, and has better accuracy assurance.
In summary, when a single optimization algorithm is used for optimizing a fan layout scheme, it is generally difficult to simultaneously ensure efficiency and precision, a multi-stage optimization process adopted for layout optimization is provided in this embodiment, an improved genetic algorithm variation rate is provided at the same time, calculation time can be saved, and the optimization process is prevented from falling into local optimization. The method provided by the embodiment has high efficiency and precision, and is suitable for the optimization problem of the fan layout scheme.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitutions or changes made by the person skilled in the art on the basis of the present invention are all within the protection scope of the present invention. The protection scope of the invention is subject to the claims.

Claims (10)

1. A wind generating set optimal layout method based on three-level dynamic variation rate is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring target wind power plant information, comprising:
(1) Drawing a rose diagram according to annual statistical wind speed and direction information in a target wind electric field region according to annual average wind resource information of the target wind power plant;
(2) The unit specification parameters comprise the height of a hub, the diameter of an impeller and a power thrust curve;
(3) The method comprises the following steps of (1) defining a wind power plant planning region boundary comprising a regular boundary or an irregular boundary;
step two: calculating the annual total average power generation and evaluating the full life cycle power cost by combining the fan wake flow speed and the turbulence model;
step three: gridding a planning region of a wind power plant, taking the leveled energy cost as an optimization target, performing unit grid position preliminary optimization by adopting an improved genetic optimization algorithm based on three-level dynamic variation rate, and obtaining a plurality of grid optimization schemes through an integration theory;
step four: and constructing the grid boundary of each candidate unit according to the preliminary optimization result of the grid position, and performing fine coordinate position optimization in the corresponding grid by adopting a self-adaptive weight particle swarm optimization algorithm to obtain a final unit layout scheme.
2. The optimized layout method of the wind generating set based on the three-level dynamic variation rate as claimed in claim 1, wherein: in the second step, the method for calculating the annual total average power generation amount and evaluating the full life cycle power cost comprises the following steps:
21 Based on the Gaussian fan wake flow model and the turbulence model, calculating the wind speed loss and turbulence enhancement of each wind driven generator after being influenced by the upstream fan wake flow;
22 Estimating the inflow wind speed at the hub height of each downstream wind driven generator by adopting a classic wake flow superposition principle, calculating the annual energy production of each wind driven generator, and summing the annual energy production of each wind driven generator to obtain the annual total energy production of the wind power plant;
23 The annual average distribution of the wind speed in the full wind direction and the total generating capacity in the full life cycle of the wind power plant are calculated, and a standardized energy cost objective function is constructed by combining economic benefit indexes comprising unit component manufacturing cost, wind power plant operation and maintenance cost and fund recovery coefficient.
3. The wind generating set optimal layout method based on the three-level dynamic variation rate according to claim 2, wherein: in the step 21), the gaussian fan wake model is:
Figure FDA0003929554600000011
Figure FDA0003929554600000012
Figure FDA0003929554600000013
Figure FDA0003929554600000021
wherein δ is wake wind deficit; delta hub The wake flow wind speed loss at the hub height is obtained; σ is the standard deviation of the wake wind speed profile; d is the fan impeller diameter; y and z are horizontal and vertical coordinates, respectively; z is a radical of hub Is the fan hub height; c T Is the thrust coefficient; k is a radical of * Is the wake attenuation coefficient; x represents the horizontal distance of the downstream fan from the upstream fan; epsilon represents a parameter related to the thrust coefficient;
the calculation method of the wake turbulence of the fan comprises the following steps:
Figure FDA0003929554600000022
wherein, I + Is additional wake turbulence; k is n Is a model constant;
the fan wake flow is superposed as follows:
Figure FDA0003929554600000023
wherein v is i And v j Is the inflow wind speed at the upstream and downstream fans; v. of 0 The incoming flow wind speed at infinity; v. of ij The inflow wind speed caused by the upstream fan at the downstream fan.
4. The wind generating set optimal layout method based on the three-level dynamic variation rate according to claim 2, wherein: in the step 22), for the inflow conditions of a given speed v and a given direction θ, the total power generation power of the wind farm is calculated according to a unit power curve, and by combining with a probability density function of a statistical average wind condition, the annual average power generation power is predicted as follows:
Figure FDA0003929554600000024
wherein N is t Is the number of fans; n is a radical of θ Is the wind direction interval division number; n is a radical of v Is the wind speed interval division number; f. of j Is the wind direction interval probability; p i Is at the wind speed v k Generating power by the unit; (X, Y) is unit coordinate information; p is a radical of j Wind speed and direction joint distribution probability, v k Is a wind speed interval division point; theta j Indicating the wind direction angle represented by the jth wind speed.
5. The wind generating set layout optimizing method based on three-level dynamic variation rate according to claim 2, characterized in that: in the step 23), the flattening energy cost objective function is:
Figure FDA0003929554600000025
wherein, C total The unit cost; c f Is the capital recovery factor; c O&M The operation and maintenance cost of the wind power plant; AEP is the annual average total power generation of the wind farm.
6. The optimized layout method of the wind generating set based on the three-level dynamic variation rate as claimed in claim 1, wherein: in the third step, the method comprises the following steps:
31 Dividing a planning area of the wind power plant into grid distribution maps with different sizes, and defaulting the coordinate position of a candidate unit to be a grid center;
32 The improved genetic optimization algorithm of the three-level dynamic variation rate is adopted, the improved variation rate of the current fan installation number, the population individual repetition rate and the current iteration step are considered at the same time, so that the global optimal search capability is improved, the local optimal situation is avoided, the gridded layout scheme is preliminarily optimized, and a plurality of grid optimization schemes are obtained through an integration theory.
7. The wind generating set optimal layout method based on the three-level dynamic variation rate as claimed in claim 6, wherein: in the step 32), the method for performing preliminary optimization of the grid position of the unit by using the improved genetic optimization algorithm based on the three-level dynamic variation rate comprises the following steps:
321 Suppose that there are two possibilities for each grid center, namely installing a fan and not installing a fan, the corresponding "genes" are respectively coded as "1" and "0", each individual corresponds to a fan arrangement scheme and consists of a string of "genes";
322 Random initialization population): for each "gene", the random amplitude is "1" or "0", and the number of iterations i step ＝0；
323 Computing individual fitness based on unique processing to obtain completely nonrepeating population individuals;
judging whether the individual fitness is smaller than a set threshold value: if so, obtaining a fan layout grid optimization scheme; if not, then i step ＝i step +1, go to step 324);
324 Random individual selection using wheel selection;
325 Random crossing is carried out on every two individuals, namely, the genes at the corresponding positions of the two individuals are interchanged;
326 Random variation of "genes" for each individual;
327 ) judge i step Whether or not it is equal to the set value N s If so, obtaining a fan layout grid optimization scheme; if not, step 323) is executed in a loop.
8. The wind generating set layout optimizing method based on three-level dynamic variation rate according to claim 7, wherein: in the step 326), the "gene" of each individual is randomly mutated by using an improved mutation rate, wherein the improved mutation rate is as follows:
Figure FDA0003929554600000031
Figure FDA0003929554600000032
wherein p is 0 A mutation rate of 0 coding positions; p is a radical of 1 1 is the rate of variation of the coding position; n is the number of grids;
Figure FDA0003929554600000033
is the average population number; r is 1 And r 2 Respectively representing the initial variation rate and the final variation rate; r is u Representing the repetition rate of individuals in the population; i.e. i step Representing the number of steps of the current iteration; n is a radical of s The total number of iteration steps is indicated.
9. The optimized layout method of the wind generating set based on the three-level dynamic variation rate as claimed in claim 1, wherein: the fourth step comprises the following steps:
41 Calculating the central coordinates and boundary conditions of the grids where the candidate set is located in all the grid optimization schemes;
42 Taking the set center coordinates of the grid optimization scheme as the initial population of the particles;
43 Setting boundary conditions as constraint conditions, and calculating a particle fitness function by taking the normalized energy cost as an optimization target;
judging whether the particle fitness function is smaller than a set threshold value: if so, obtaining a layout scheme of the wind generating set; if not, adding 1 to the iteration number, and executing the step 44);
44 Judging whether the iteration number reaches the set maximum iteration number: if so, obtaining a layout scheme of the wind generating set; if not, updating the particle population by adopting a self-adaptive weight particle swarm optimization algorithm, and executing the step 43).
10. The optimized layout method of the wind generating set based on the three-level dynamic variation rate as claimed in claim 1, wherein: in the step 44), the adaptive weight particle swarm optimization algorithm is as follows:
Figure FDA0003929554600000041
Figure FDA0003929554600000042
wherein the content of the first and second substances,
Figure FDA0003929554600000043
is the position of the ith particle at the time of the t-th iteration;
Figure FDA0003929554600000044
is the velocity vector of the ith particle at the t-th iteration; r is 1 And r 2 Is a random number between 0 and 1; p is a radical of i And p g The best candidate solutions found for the ith particle and the entire population, respectively; beta is a i And beta g The parameters are self-defined parameters and respectively control the development capability and the exploration capability of particle motion; omega a Is the nonlinear dynamic inertial weight adjusted according to the distance to the global optimal solution during the optimization process.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611961A (en) * 2023-07-21 2023-08-18 电子科技大学中山学院 Micro site selection and fan selection collaborative optimization method for offshore wind farm
CN116664335A (en) * 2023-07-24 2023-08-29 创域智能(常熟)网联科技有限公司 Intelligent monitoring-based operation analysis method and system for semiconductor production system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611961A (en) * 2023-07-21 2023-08-18 电子科技大学中山学院 Micro site selection and fan selection collaborative optimization method for offshore wind farm
CN116611961B (en) * 2023-07-21 2023-12-19 电子科技大学中山学院 Micro site selection and fan selection collaborative optimization method for offshore wind farm
CN116664335A (en) * 2023-07-24 2023-08-29 创域智能(常熟)网联科技有限公司 Intelligent monitoring-based operation analysis method and system for semiconductor production system
CN116664335B (en) * 2023-07-24 2023-10-03 创域智能(常熟)网联科技有限公司 Intelligent monitoring-based operation analysis method and system for semiconductor production system

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